





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			IFInpaintingPipeline
     __magic_name__   =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
     __magic_name__   =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __magic_name__   =			PipelineTesterMixin.required_optional_params - {'latents'}




     def        lowerCAmelCase__		(    self							):
         return self._get_dummy_components()




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0							):
         if str(snake_case_							).startswith('mps'							):
             _A     =							torch.manual_seed(snake_case_							)
         else:
             _A     =							torch.Generator(device=snake_case_							).manual_seed(snake_case_							)

         _A     =							floats_tensor((1, 3, 32, 32)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)
         _A     =							floats_tensor((1, 3, 32, 32)				,     rng=random.Random(snake_case_							)							).to(snake_case_							)

         _A     =							{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'mask_image': mask_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()				,     reason='XFormers attention is only available with CUDA and `xformers` installed'				,     )
     def        lowerCAmelCase__		(    self							):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)




     def        lowerCAmelCase__		(    self							):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != 'cuda'				,     reason='float16 requires CUDA'							)
     def        lowerCAmelCase__		(    self							):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1							)




     def        lowerCAmelCase__		(    self							):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)




     def        lowerCAmelCase__		(    self							):
         self._test_save_load_local()




     def        lowerCAmelCase__		(    self							):
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2				,     )







def a__       (			A_   ):






  '''simple docstring'''





  if len(A_   ) < 2:
    return collection

  def circle_sort_util(A_,	A_,	A_   ) -> bool:
    __magic_name__								=			False

    if low == high:
      return swapped

    __magic_name__								=			low
    __magic_name__								=			high

    while left < right:
      if collection[left] > collection[right]:
        __magic_name__	,       __magic_name__								=			(
            collection[right],
            collection[left],
        )
        __magic_name__								=			True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __magic_name__	,       __magic_name__								=			(
          collection[right + 1],
          collection[left],
      )

      __magic_name__								=			True

    __magic_name__								=			low + int((high - low) / 2   )
    __magic_name__								=			circle_sort_util(A_,	A_,	A_   )
    __magic_name__								=			circle_sort_util(A_,	mid + 1,	A_   )

    return swapped or left_swap or right_swap

  __magic_name__								=			True

  while is_not_sorted is True:
    __magic_name__								=			circle_sort_util(A_,	0,	len(A_   ) - 1   )

  return collection


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	Tuple       			=  [int(item) for item in user_input.split(',')]
    print(circle_sort(unsorted))

